AI-based inverse design for electrochemically controlled microscopic gradients of O2 and H2O2

A fundamental understanding of the extracellular microenvironments of O2 and reactive oxygen species (ROS) such as H2O2, ubiquitous in microbiology, demands high-throughput methods of mimicking, controlling, and perturbing gradients of O2 and H2O2 at microscopic scale with high spatiotemporal precision. However, there is a paucity for a high-throughput strategy of microenvironment design and it remains challenging to achieve O2 and H2O2 heterogeneities with the microbiologically desirable spatiotemporal resolutions. Here we report machine-learning-based inverse design of electrochemically generated microscopic O2 and H2O2 profiles that are relevant for microbiology. Microwire arrays with suitably designed electrochemical catalysts enable the independent control of O2 and H2O2 profiles with spatial resolution of ~101 μm and temporal resolution of ~100 sec. Neural networks aided by data augmentation inversely design the experimental conditions needed for targeted O2 and H2O2 microenvironments while being order-of-magnitude faster. Integrating artificial intelligence with electrochemically controlled concentration heterogeneity creates a viable fast-response platform towards better understanding the extracellular space with desirable spatiotemporal control.